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» Results of the KDD'99 Classifier Learning
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KDD
2002
ACM
157views Data Mining» more  KDD 2002»
14 years 10 months ago
Exploiting unlabeled data in ensemble methods
An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between a...
Kristin P. Bennett, Ayhan Demiriz, Richard Maclin
IJCAI
2001
13 years 11 months ago
Probabilistic Classification and Clustering in Relational Data
Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best des...
Benjamin Taskar, Eran Segal, Daphne Koller
KES
2006
Springer
13 years 10 months ago
The Performance of LVQ Based Automatic Relevance Determination Applied to Spontaneous Biosignals
The issue of Automatic Relevance Determination (ARD) has attracted attention over the last decade for the sake of efficiency and accuracy of classifiers, and also to extract knowle...
Martin Golz, David Sommer
CNSM
2010
13 years 8 months ago
An investigation on the identification of VoIP traffic: Case study on Gtalk and Skype
The classification of encrypted traffic on the fly from network traces represents a particularly challenging application domain. Recent advances in machine learning provide the opp...
Riyad Alshammari, A. Nur Zincir-Heywood
CVPR
2009
IEEE
15 years 5 months ago
An Instance Selection Approach to Multiple Instance Learning
Multiple-instance Learning (MIL) is a new paradigm of supervised learning that deals with the classification of bags. Each bag is presented as a collection of instances from whi...
Zhouyu Fu (Australian National University), Antoni...